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Nowadays, intelligent transportation system (ITS) has become one of the most popular subjects of scientific research. ITS provides innovative services to traffic monitoring. The classification of emergency vehicles in traffic surveillance cameras provides an early warning to ensure a rapid reaction in emergency events. Computer vision technology, including deep learning, has many advantages for traffic monitoring. For instance, convolutional neural network (CNN) has given very good results and optimal performance in computer vision tasks, such as the classification of vehicles according to their types, and brands. In this paper, we will classify emergency vehicles from the output of a closed-circuit television (CCTV) camera. Among the advantages of this research paper is providing detailed information on the emergency vehicle classification topic. Emergency vehicles have the highest priority on the road and finding the best emergency vehicle classification model in realtime will undoubtedly save lives. Thus, we have used eight CNN architectures and compared their performances on the Analytics Vidhya Emergency Vehicle dataset. The experiments show that the utilization of DenseNet121 gives excellent classification results which makes it the most suitable architecture for this research topic, besides, DenseNet121 does not require a high memory size which makes it appropriate for real-time applications.<p> </p>

No abstract

Image memorability represents the degree to which images are remembered or forgotten after a period of time. Studying image memorability in computer vision is the task of finding special characteristics in memorable images, in order to develop a representative model of this type of images. Several approaches have been realised to examine features that can affect image memorability. In this study, the authors use bag‐of‐features as another kind of visual feature descriptor to assess image memorability. The authors’ method based on bag‐of‐visual‐words (BoVWs) technique involves four main steps. First, the authors extract local image features from regions/points of interest which are automatically detected. Then, they encode these local features by mapping them to a created visual vocabulary. Later, the authors apply features pooling and normalisation techniques to obtain image BoVW representation. Finally, the authors use this representation to examine image memorability as a problem of classification. They present different implementation choices for each step and compare reached results. The authors’ method performs best significant results in comparison with other approaches found in literature.

Photos are becoming more spread with digital age. Cameras, smart phones and Internet provide large dataset of images available to a wide audience. Assessing memorability of these photos is becoming a challenging task. Besides, finding the best representative model for memorable images will enable memorability prediction. The authors develop a new approachbased rule of photography to evaluate image memorability. In fact, they use three groups of features: image basic features, layout features and image composition features. In addition, they introduce a diversified panel of classifiers based on some data mining techniques used for memorability analysis. They experiment their proposed approach and they compare its results to the state-of-the-art approaches dealing with image memorability. Their approach experiment's results prove that models used in their approach are encouraging predictors for image memorability.